A novel HCP (heparin-binding protein-C reactive protein-procalcitonin) inflammatory composite model can predict severe acute pancreatitis

Severe acute pancreatitis (SAP) presents with an aggressive clinical presentation and high lethality rate. Early prediction of the severity of acute pancreatitis will help physicians to further precise treatment and improve intervention. This study aims to construct a composite model that can predict SAP using inflammatory markers. 212 patients with acute pancreatitis enrolled from January 2018 to June 2020 were included in this study, basic parameters at admission and 24 h after hospitalization, and laboratory results such as inflammatory markers were collected. Pearson's test was used to analyze the correlation between heparin-binding protein (HBP), procalcitonin (PCT), and C-reactive protein (CRP). Risk factors affecting SAP were analyzed using multivariate logistic regression, inflammatory marker models were constructed, and subject operating curves were used to verify the discrimination of individual as well as inflammatory marker models and to find the optimal cut-off value based on the maximum Youden index. In the SAP group, the plasma levels of HBP, CRP, and PCT were 139.1 ± 74.8 ng/mL, 190.7 ± 106.3 mg/L and 46.3 ± 22.3 ng/mL, and 25.3 ± 16.0 ng/mL, 145.4 ± 67.9 mg/L and 27.9 ± 22.4 ng/mL in non-SAP patients, with a statistically significant difference between the two groups (P < 0.001), The Pearson correlation analysis showed a positive correlation between the three values of HBP, CRP, and PCT. The results of the multivariate logistic regression analysis showed that HBP (OR = 1.070 [1.044–1.098], P < 0.001), CRP (OR = 1.010 [1.004–1.016], P = 0.001), and PCT (OR = 1.030[1.007–1.053], P < 0.001) were risk factors for SAP, and the area under the curve of the HBP-CRP-PCT model was 0.963 (0.936–0.990). The HCP model, consisting of HBP, CRP, and PCT; is well differentiated and easy to use and can predict the risk of SAP in advance.

Measurement of plasma HBP, PCT, and CRP levels. HBP, also known as cationic repressor protein, is mainly found in neutrophil secretory granules and asplenophil granules, and studies have shown that it plays a specific role in the regulation of the inflammatory response and vascular leakage; PCT is a pre-peptide of calcitonin, which is a glycoprotein in nature and has no hormonal activity. Under normal metabolism of the body, PCT is mainly secreted and produced by parafollicular cells of the thyroid gland, and its content in the serum is small; if bacterial infection or sepsis occurs in the body, liver macrophages and neuroendocrine cells can secrete PCT and increase the content of this substance in the serum; CRP is an acute temporal response protein produced by hepatocytes stimulated by cytokines secreted by giant cell activation and is an essential component of the body's intrinsic immune system. Patients were collected within 1-2 days of admission. Before sample collection, patients avoided strenuous exercise and were placed in a supine position. 2 ml of fasting venous blood was collected using EDTA-K2 anticoagulated vacuum blood collection tubes and inert separator gel pro-coagulation tubes; routine blood tests were performed within 30 min for EDTA-K2 anticoagulated blood samples, and the inert separator gel pro-coagulation tubes were centrifuged at 3000 r/min for 10 min, and the supernatant was taken for CRP, PCT and HBP tests. All blood samples were tested within 2 h at room temperature (25 °C). Routine blood tests were performed by Xisenmecan XE-2100 automatic hematology analyzer; CRP and PCT were performed by Beckman Coulter AU5800 automatic biochemical analysis system using the immunoturbidimetric method and immunofluorescence method, respectively. HBP was detected by ELISA, the detection instrument was an automatic enzyme standardization instrument (BIORAD, USA), and the reagents were enzyme-linked immunosorbent assay kits (Hangzhou Zhong Han Shengtai Biotechnology Co., Ltd. and Changchun Bode Technology & Biology Co., Ltd.), and the whole test operation was carried out in strict accordance with the steps or conditions specified in the relevant operating instructions of the kits and instruments.
Clinical data collection. Clinical data on AP patients meeting inclusion and exclusion criteria were collected from the Cangzhou Central Hospital medical record system, (1) Basic hospitalization data: gender, age, Body Mass Index (BMI), etiology, medical history, etc. (2) Laboratory tests within 24 h of admission: leukocytes, hemoglobin, platelets, urea, serum creatinine, bilirubin, HBP, CRP, and PCT (3) Complications: organ failure, infectious necrosis, persistent organ failure (4) Past medical history: hypertension, diabetes mellitus, fatty liver, smoking, alcohol consumption. Data analysis. The t test was used for continuous variables that conformed to a normal distribution, and the Wilcoxon rank sum test was used for continuous variables that did not conform to a normal distribution. All categorical variables were tested with the Chi-square test or Fisher's exact test. Variables with P < 0.05 were first screened by univariate logistic regression analysis and then included in multivariate logistic regression analysis to screen out risk factors and construct a composite inflammatory index model. The optimal cut-off values for different inflammatory indices were calculated by selecting the number corresponding to the maximum Youden index according to sensitivity and specificity, and the receiver operating characteristic curve (ROC) was used to determine the differentiation of individual inflammatory indices as well as the integrated inflammatory model for SAP.
Data were analyzed using SPSS 25.0 (IBM, Armonk, New York, USA), and calculated P values < 0.05 (both sides) were considered statistically significant. Graphs were plotted using R language (version 4.0.5) and Graph-Pad Prism (version: 8.0). Sample size estimation was performed using PASS (version: 11.0) prior to the study.

Results
Baseline information of severe acute pancreatitis and non-severe pancreatitis patients. A total of 212 patients were included in the study (Fig. 1), 92 in the SAP group and 120 in the Non-SAP group, with statistically significant differences in platelet count, BUN, serum creatinine, bilirubin, and international normalized ratio (INR) between the two groups in the laboratory tests (P < 0.001); In terms of complications, again there were statistically significant differences between the two groups, with 73 (79.3%) of the SAP patients experiencing organ failure, 55 (59.8%) experiencing infected necrosis and 70 (76.1%) experiencing persistent organ failure, all at a high rate; In termso of past medical history, 26 (Fig. 2). The rest of the detailed data is shown in Table 1.

Correlation analysis between the scores of the three inflammatory indicators. By analyzing
the two-by-two Pearson coefficients between the values of the three inflammatory indices, the results showed that the Pearson coefficient was 0.374 (P < 0.001) between HBP and CRP, 0.327 (P < 0.001) between HBP and PCT, 0.212 (P = 001),0.002 between CRP and PCT. There was a positive correlation between the three indices ( Table 2).     Table 3). The HCP inflammatory index model was constructed based on the results of logistic regression with the formula = 6.850 − 0.068 × HBP (ng/mL) − 0.010 × CRP (mg/mL) − 0.029 × PCT (ng/mL).  (Table 4).  Fig. 1).

Discussion
In this study, a logistic model for predicting severe pancreatitis was constructed from three inflammatory indices (HBP, CRP, and PCT) in 212 patients. First, we found that all three inflammatory indices were clearly and statistically significantly correlated with the development of severe pancreatitis. Secondly, the combination of three biomarkers, HBP-CRP-PCT, could effectively distinguish severe pancreatitis from non-severe pancreatitis, and the area under the curve of the model was greater than 0.8. At present, there are many various clinical evaluation indicators for severe pancreatitis, such as clinical manifestations, computed tomography scans, various scoring systems, and laboratory test indices 15,16 . However, there are no unified accurate diagnostic indices for severe pancreatitis. The disease of patients with severe pancreatitis progresses rapidly, and finding a test that can timely and accurately determine the patient's condition is essential for clinical treatment and prognosis, which is also the core of the treatment of clinical acute severe pancreatitis [17][18][19][20] . It is generally accepted that inflammatory markers play an essential role in the development of severe pancreatitis. Giudice et al. 3 suggest that CRP is an essential inflammatory indicator and affects various physiological processes. When inflammation or infection occurs, CRP concentrations rise rapidly and repair cellular tissues, reducing damage and increasing resistance to inflammation. Sager et al. 21 evaluated several randomized controlled trials and they concluded that PCT kinetics was an indicator that was shown to correlate with the severity of pancreatitis and the degree of disease regression and that PCT had unique advantages in the management of patients. Kahn et al. 22 investigated patients in the emergency department and they found that HBP showed good discrimination in detecting the most severe patients and also played a specific role in regulating the inflammatory response and vascular infiltration. Therefore the use of multiple inflammatory markers is necessary for the prediction of severe pancreatitis.
We constructed an inflammatory integrated logistic regression model using three inflammatory markers, heparin-binding protein, C-reactive protein, and procalcitonin. We clarified its differentiation by plotting the ROC curve, and the area under the curve of HCP was 0.963, indicating that the model can predict severe pancreatitis. Some previous studies have also combined inflammatory indicators to predict other diseases [23][24][25] . Niu et al. 26 used time-resolved fluorescence immunoassay for PCT, CRP, and serum amyloid A1 and combined these three to determine early infection. The area under the curve of the combined assay was greater than 0.8. Thus this combined biomarker test could improve the diagnostic accuracy of early infection. Ma et al. 27 used the combination of HBP and CRP to diagnose bacterial respiratory infections, and the AUC reached 0.797, also reflecting a good differentiation. This combined diagnostic score can guide the clinical formulation of a reasonable treatment plan. Yang et al. 28 also used a triad of serum inflammatory markers to diagnose acute bacterial upper respiratory tract infections in children. This combined diagnostic score is expected to be a potential predictor of outcome.  Table 4. Predictive value of independent predictors and joint predictors for non-SAP and SAP. SAP severe acute pancreatitis, HBP heparin-binding protein, CRP C-reactive protein, PCT procalcitonin, AUC areas under the receiver operating characteristic curve, PPV positive predictive value, NPV negative predictive value.

Predictors
Optimal cut-off value AUC Accuracy Sensitivity Specificity PPV NPV www.nature.com/scientificreports/ At the same time, a positive correlation was found between the expression of HBP, PCT, and CRP, which is also consistent with our study. We applied inflammatory indices to the prediction of severe pancreatitis for the first time. We found that combining multiple inflammatory indices could also predict SAP, a finding that expands the usefulness of combined inflammatory index testing. This study has several limitations, firstly, this study is retrospective, and there is patient selection bias and confounding bias. Secondly, the data in this study are from a single center, and future studies may need to be conducted with an expanded sample size from multiple centers. Then, the established inflammatory co-detection model needs to be validated by multiple external centers to expand its applicability.

Conclusion
In conclusion, the HCP model, which combines multiple inflammatory markers, is highly valuable for diagnosing severe acute pancreatitis and can serve as a reference for clinical management of SAP. This model aids in reducing underdiagnosis rates and enables early prediction of patient prognosis, thereby facilitating focused monitoring and treatment of high-risk patients. Overall, implementation of the HCP model can significantly improve comprehensive clinical management of SAP, enhance early diagnosis of severe acute pancreatitis, minimize delays in patient treatment, reduce adverse events, conserve medical resources, and lower patient expenses.

Data availability
The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.